MIFS-ND: A mutual information-based feature selection method

N. Hoque, D.K. Bhattacharyya, J.K. Kalita
2014 Expert systems with applications  
Feature selection is used to choose a subset of relevant features for effective classification of data. In high dimensional data classification, the performance of a classifier often depends on the feature subset used for classification. In this paper, we introduce a greedy feature selection method using mutual information. This method combines both feature-feature mutual information and featureclass mutual information to find an optimal subset of features to minimize redundancy and to maximize
more » ... relevance among features. The effectiveness of the selected feature subset is evaluated using multiple classifiers on multiple datasets. The performance of our method both in terms of classification accuracy and execution time performance, has been found significantly high for twelve real-life datasets of varied dimensionality and number of instances when compared with several competing feature selection techniques.
doi:10.1016/j.eswa.2014.04.019 fatcat:u5nlcyf7lndhfo5uheleopqsdq